<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a reform of the teaching methodology based on the exercise prescription course centered on intelligent exercise prescription recommendation and designs a specific implementation plan. A criterion function is used to determine and represent the constant terms of the user-set data using the K-means clustering algorithm. The personal data of users is added on this basis, and the attribute weighting is used to calculate user similarity and construct the user matrix. The core parameters of intelligentized exercise prescription are examined, and the relevant data of the exerciser group is obtained through modeling. The set of similar cases is determined, and the exercise effects in the set of similar cases are fused and calculated, and the effect level of the fused effects in the cases is discriminated. The model developed in this paper is implemented in the college physical education classroom, and the student’s attitudes toward physical exercise and fitness levels are analyzed. The results show that the attitude towards physical exercise behavior of students in group A is significantly better than that of those in group B. The significance test (F = 7.985, Sig = 0.005) is less than 0.05. Taking the 50-meter sprint as an example, the difference in the duration between male students and female students in groups A and B is 0.83s and 0.9s, respectively, which is a large enhancement, which shows that the intelligent exercise prescription has a positive impact on physical education teaching.</p>
</abstract>

This paper proposes a reform of the teaching methodology based on the exercise prescription course centered on intelligent exercise prescription recommendation and designs a specific implementation plan. A criterion function is used to determine and represent the constant terms of the user-set data using the K-means clustering algorithm. The personal data of users is added on this basis, and the attribute weighting is used to calculate user similarity and construct the user matrix. The core parameters of intelligentized exercise prescription are examined, and the relevant data of the exerciser group is obtained through modeling. The set of similar cases is determined, and the exercise effects in the set of similar cases are fused and calculated, and the effect level of the fused effects in the cases is discriminated. The model developed in this paper is implemented in the college physical education classroom, and the student’s attitudes toward physical exercise and fitness levels are analyzed. The results show that the attitude towards physical exercise behavior of students in group A is significantly better than that of those in group B. The significance test (F = 7.985, Sig = 0.005) is less than 0.05. Taking the 50-meter sprint as an example, the difference in the duration between male students and female students in groups A and B is 0.83s and 0.9s, respectively, which is a large enhancement, which shows that the intelligent exercise prescription has a positive impact on physical education teaching.

Research on the Path of Enhancing Physical Education Teaching in Colleges and Universities in the Internet Era

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper proposes a reform of the teaching methodology based on the exercise prescription course centered on intelligent exercise prescription recommendation...

Research on the Path of Enhancing Physical Education Teaching in Colleges and Universities in the Internet Era

Publié en ligne: 05 août 2024

Pages: -

Reçu: 11 avr. 2024

Accepté: 10 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2076

Mots clés
K-mean clustering algorithm, Criterion function, Attribute weighting calculation, User similarity, Exercise prescription, Physical education teaching

© 2024 Yanting Long et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Path of Enhancing Physical Education Teaching in Colleges and Universities in the Internet Era

Publié en ligne: 05 août 2024

Pages: -

Reçu: 11 avr. 2024

Accepté: 10 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2076

Mots clésK-mean clustering algorithm, Criterion function, Attribute weighting calculation, User similarity, Exercise prescription, Physical education teaching

© 2024 Yanting Long et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
K-mean clustering algorithm, Criterion function, Attribute weighting calculation, User similarity, Exercise prescription, Physical education teaching